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https://github.com/BerriAI/litellm.git
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raise exception if optional param is not mapped to model
This commit is contained in:
Krrish Dholakia
parent
49f65b7eb8
commit
1cae080eb2
8 changed files with 155 additions and 112 deletions
172
litellm/utils.py
172
litellm/utils.py
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@ -931,59 +931,62 @@ def get_optional_params( # use the openai defaults
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# 12 optional params
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functions=[],
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function_call="",
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temperature=1,
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top_p=1,
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n=1,
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temperature=None,
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top_p=None,
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n=None,
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stream=False,
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stop=None,
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max_tokens=float("inf"),
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presence_penalty=0,
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max_tokens=None,
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presence_penalty=None,
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frequency_penalty=0,
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logit_bias={},
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num_beams=1,
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remove_input=False, # for nlp_cloud
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user="",
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deployment_id=None,
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model=None,
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custom_llm_provider="",
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top_k=40,
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return_full_text=False,
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task=None,
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**kwargs
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):
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# retrieve all parameters passed to the function
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passed_params = locals()
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special_params = passed_params.pop("kwargs")
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for k, v in special_params.items():
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passed_params[k] = v
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default_params = {
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"functions":[],
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"function_call":"",
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"temperature":1,
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"top_p":1,
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"n":1,
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"stream":False,
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"temperature":None,
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"top_p":None,
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"n":None,
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"stream":None,
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"stop":None,
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"max_tokens":float("inf"),
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"presence_penalty":0,
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"frequency_penalty":0,
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"max_tokens":None,
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"presence_penalty":None,
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"frequency_penalty":None,
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"logit_bias":{},
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"num_beams":1,
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"remove_input":False, # for nlp_cloud
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"user":"",
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"deployment_id":None,
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"model":None,
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"custom_llm_provider":"",
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"top_k":40,
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"return_full_text":False,
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"task":None,
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}
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# filter out those parameters that were passed with non-default values
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non_default_params = {k: v for k, v in passed_params.items() if v != default_params[k]}
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non_default_params = {k: v for k, v in passed_params.items() if (k != "model" and k != "custom_llm_provider" and k in default_params and v != default_params[k])}
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## raise exception if function calling passed in for a provider that doesn't support it
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if "functions" in non_default_params or "function_call" in non_default_params:
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if custom_llm_provider != "openai" and custom_llm_provider != "text-completion-openai" and custom_llm_provider != "azure":
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raise ValueError("LiteLLM.Exception: Function calling is not supported by this provider")
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def _check_valid_arg(supported_params):
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unsupported_params = [k for k in non_default_params.keys() if k not in supported_params]
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if unsupported_params:
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raise ValueError("LiteLLM.Exception: Unsupported parameters passed: {}".format(', '.join(unsupported_params)))
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## raise exception if provider doesn't support passed in param
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optional_params = {}
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if custom_llm_provider == "anthropic":
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## check if unsupported param passed in
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supported_params = ["stream", "stop", "temperature", "top_p", "max_tokens"]
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_check_valid_arg(supported_params=supported_params)
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# handle anthropic params
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if stream:
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optional_params["stream"] = stream
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@ -997,6 +1000,9 @@ def get_optional_params( # use the openai defaults
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optional_params["max_tokens_to_sample"] = max_tokens
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return optional_params
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elif custom_llm_provider == "cohere":
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## check if unsupported param passed in
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supported_params = ["stream", "temperature", "max_tokens", "logit_bias"]
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_check_valid_arg(supported_params=supported_params)
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# handle cohere params
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if stream:
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optional_params["stream"] = stream
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@ -1008,6 +1014,10 @@ def get_optional_params( # use the openai defaults
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optional_params["logit_bias"] = logit_bias
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return optional_params
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elif custom_llm_provider == "replicate":
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## check if unsupported param passed in
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supported_params = ["stream", "temperature", "max_tokens", "top_p", "stop"]
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_check_valid_arg(supported_params=supported_params)
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if stream:
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optional_params["stream"] = stream
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return optional_params
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@ -1020,11 +1030,13 @@ def get_optional_params( # use the openai defaults
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optional_params["temperature"] = temperature
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if top_p != 1:
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optional_params["top_p"] = top_p
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if top_k != 40:
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optional_params["top_k"] = top_k
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if stop != None:
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optional_params["stop_sequences"] = stop
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elif custom_llm_provider == "huggingface":
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## check if unsupported param passed in
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supported_params = ["stream", "temperature", "max_tokens", "top_p", "stop", "return_full_text", "details"]
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_check_valid_arg(supported_params=supported_params)
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if temperature != 1:
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optional_params["temperature"] = temperature
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if top_p != 1:
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@ -1042,16 +1054,17 @@ def get_optional_params( # use the openai defaults
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optional_params["repetition_penalty"] = presence_penalty
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optional_params["return_full_text"] = return_full_text
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optional_params["details"] = True
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optional_params["task"] = task
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elif custom_llm_provider == "together_ai":
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## check if unsupported param passed in
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supported_params = ["stream", "temperature", "max_tokens", "top_p", "stop", "frequency_penalty"]
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_check_valid_arg(supported_params=supported_params)
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if stream:
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optional_params["stream_tokens"] = stream
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if temperature != 1:
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optional_params["temperature"] = temperature
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if top_p != 1:
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optional_params["top_p"] = top_p
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if top_k != 40:
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optional_params["top_k"] = top_k
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if max_tokens != float("inf"):
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optional_params["max_tokens"] = max_tokens
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if frequency_penalty != 0:
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@ -1059,41 +1072,29 @@ def get_optional_params( # use the openai defaults
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if stop != None:
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optional_params["stop"] = stop #TG AI expects a list, example ["\n\n\n\n","<|endoftext|>"]
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elif custom_llm_provider == "palm":
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## check if unsupported param passed in
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supported_params = ["temperature", "top_p"]
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_check_valid_arg(supported_params=supported_params)
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if temperature != 1:
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optional_params["temperature"] = temperature
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if top_p != 1:
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optional_params["top_p"] = top_p
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elif (
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model in litellm.vertex_chat_models or model in litellm.vertex_code_chat_models
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): # chat-bison has diff args from chat-bison@001, ty Google :)
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custom_llm_provider == "vertex_ai"
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):
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## check if unsupported param passed in
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supported_params = ["temperature", "top_p", "max_tokens", "stream"]
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_check_valid_arg(supported_params=supported_params)
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if temperature != 1:
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optional_params["temperature"] = temperature
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if top_p != 1:
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optional_params["top_p"] = top_p
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if stream:
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optional_params["stream"] = stream
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if max_tokens != float("inf"):
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optional_params["max_output_tokens"] = max_tokens
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elif model in litellm.vertex_text_models:
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# required params for all text vertex calls
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# temperature=0.2, top_p=0.1, top_k=20
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# always set temperature, top_p, top_k else, text bison fails
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optional_params["temperature"] = temperature
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optional_params["top_p"] = top_p
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optional_params["top_k"] = top_k
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if max_tokens != float("inf"):
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optional_params["max_output_tokens"] = max_tokens
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elif model in model in litellm.vertex_code_text_models:
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optional_params["temperature"] = temperature
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if max_tokens != float("inf"):
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optional_params["max_output_tokens"] = max_tokens
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elif custom_llm_provider == "baseten":
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optional_params["temperature"] = temperature
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optional_params["stream"] = stream
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if top_p != 1:
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optional_params["top_p"] = top_p
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optional_params["top_k"] = top_k
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optional_params["num_beams"] = num_beams
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if max_tokens != float("inf"):
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optional_params["max_new_tokens"] = max_tokens
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elif custom_llm_provider == "sagemaker":
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if "llama-2" in model:
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# llama-2 models on sagemaker support the following args
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@ -1103,14 +1104,24 @@ def get_optional_params( # use the openai defaults
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top_p: In each step of text generation, sample from the smallest possible set of words with cumulative probability top_p. If specified, it must be a float between 0 and 1.
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return_full_text: If True, input text will be part of the output generated text. If specified, it must be boolean. The default value for it is False.
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"""
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## check if unsupported param passed in
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supported_params = ["temperature", "max_tokens"]
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_check_valid_arg(supported_params=supported_params)
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if max_tokens != float("inf"):
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optional_params["max_new_tokens"] = max_tokens
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if temperature != 1:
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optional_params["temperature"] = temperature
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if top_p != 1:
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optional_params["top_p"] = top_p
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else:
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## check if unsupported param passed in
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supported_params = []
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_check_valid_arg(supported_params=supported_params)
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elif custom_llm_provider == "bedrock":
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if "ai21" in model:
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supported_params = ["max_tokens", "temperature", "stop", "top_p"]
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_check_valid_arg(supported_params=supported_params)
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# params "maxTokens":200,"temperature":0,"topP":250,"stop_sequences":[],
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# https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=j2-ultra
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if max_tokens != float("inf"):
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@ -1122,8 +1133,10 @@ def get_optional_params( # use the openai defaults
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if top_p != 1:
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optional_params["topP"] = top_p
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elif "anthropic" in model:
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supported_params = ["max_tokens", "temperature", "stop", "top_p"]
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_check_valid_arg(supported_params=supported_params)
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# anthropic params on bedrock
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# \"max_tokens_to_sample\":300,\"temperature\":0.5,\"top_k\":250,\"top_p\":1,\"stop_sequences\":[\"\\\\n\\\\nHuman:\"]}"
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# \"max_tokens_to_sample\":300,\"temperature\":0.5,\"top_p\":1,\"stop_sequences\":[\"\\\\n\\\\nHuman:\"]}"
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if max_tokens != float("inf"):
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optional_params["max_tokens_to_sample"] = max_tokens
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else:
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@ -1132,11 +1145,11 @@ def get_optional_params( # use the openai defaults
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optional_params["temperature"] = temperature
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if top_p != 1:
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optional_params["top_p"] = top_p
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if top_k != 40:
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optional_params["top_k"] = top_k
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if stop != None:
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optional_params["stop_sequences"] = stop
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elif "amazon" in model: # amazon titan llms
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supported_params = ["max_tokens", "temperature", "stop", "top_p"]
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_check_valid_arg(supported_params=supported_params)
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# see https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=titan-large
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if max_tokens != float("inf"):
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optional_params["maxTokenCount"] = max_tokens
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@ -1148,14 +1161,14 @@ def get_optional_params( # use the openai defaults
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optional_params["topP"] = top_p
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elif model in litellm.aleph_alpha_models:
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supported_params = ["max_tokens", "stream", "top_p", "temperature", "presence_penalty", "frequency_penalty", "n", "stop"]
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_check_valid_arg(supported_params=supported_params)
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if max_tokens != float("inf"):
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optional_params["maximum_tokens"] = max_tokens
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if stream:
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optional_params["stream"] = stream
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if temperature != 1:
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optional_params["temperature"] = temperature
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if top_k != 40:
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optional_params["top_k"] = top_k
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if top_p != 1:
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optional_params["top_p"] = top_p
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if presence_penalty != 0:
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@ -1167,29 +1180,28 @@ def get_optional_params( # use the openai defaults
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if stop != None:
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optional_params["stop_sequences"] = stop
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elif model in litellm.nlp_cloud_models or custom_llm_provider == "nlp_cloud":
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supported_params = ["max_tokens", "stream", "temperature", "top_p", "presence_penalty", "frequency_penalty", "n", "stop"]
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_check_valid_arg(supported_params=supported_params)
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if max_tokens != float("inf"):
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optional_params["max_length"] = max_tokens
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if stream:
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optional_params["stream"] = stream
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if temperature != 1:
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optional_params["temperature"] = temperature
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if top_k != 40:
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optional_params["top_k"] = top_k
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if top_p != 1:
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optional_params["top_p"] = top_p
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if presence_penalty != 0:
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optional_params["presence_penalty"] = presence_penalty
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if frequency_penalty != 0:
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optional_params["frequency_penalty"] = frequency_penalty
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if num_beams != 1:
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optional_params["num_beams"] = num_beams
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if n != 1:
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optional_params["num_return_sequences"] = n
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if remove_input == True:
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optional_params["remove_input"] = True
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if stop != None:
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optional_params["stop_sequences"] = stop
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elif model in litellm.petals_models or custom_llm_provider == "petals":
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supported_params = ["max_tokens", "temperature", "top_p"]
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_check_valid_arg(supported_params=supported_params)
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# max_new_tokens=1,temperature=0.9, top_p=0.6
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if max_tokens != float("inf"):
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optional_params["max_new_tokens"] = max_tokens
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@ -1200,33 +1212,13 @@ def get_optional_params( # use the openai defaults
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if top_p != 1:
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optional_params["top_p"] = top_p
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else: # assume passing in params for openai/azure openai
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if functions != []:
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optional_params["functions"] = functions
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if function_call != "":
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optional_params["function_call"] = function_call
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if temperature != 1:
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optional_params["temperature"] = temperature
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if top_p != 1:
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optional_params["top_p"] = top_p
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if n != 1:
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optional_params["n"] = n
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if stream:
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optional_params["stream"] = stream
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if stop != None:
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optional_params["stop"] = stop
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if max_tokens != float("inf"):
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optional_params["max_tokens"] = max_tokens
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if presence_penalty != 0:
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optional_params["presence_penalty"] = presence_penalty
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if frequency_penalty != 0:
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optional_params["frequency_penalty"] = frequency_penalty
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if logit_bias != {}:
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optional_params["logit_bias"] = logit_bias
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if user != "":
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optional_params["user"] = user
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if deployment_id != None:
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optional_params["deployment_id"] = deployment_id
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return optional_params
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supported_params = ["functions", "function_call", "temperature", "top_p", "n", "stream", "stop", "max_tokens", "presence_penalty", "logit_bias", "user", "deployment_id"]
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_check_valid_arg(supported_params=supported_params)
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optional_params = non_default_params
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# if user passed in non-default kwargs for specific providers/models, pass them along
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for k in passed_params.keys():
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if k not in default_params.keys():
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optional_params[k] = passed_params[k]
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return optional_params
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def get_llm_provider(model: str, custom_llm_provider: Optional[str] = None):
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